Many renewable energy sources provide intermittent power that can fluctuate with weather and time of day. Wind power can vary considerably with the seasons. Solar power is affected by cloud cover, and obviously can’t produce power at night. Hydro power is also somewhat variable dependent on rainfall levels. These fluctuations of power supply need to be factored much differently than the traditional power sources that can be dispatched and can be made to produce more power as demand increases.
As more of these intermittent energy sources come online, it becomes increasingly important to understand the base line of power that these sources can generate. Significant seasonal increases of renewable power would allow a utility to decrease their power generation from less clean sources. Prolonged weather events can curtail renewable power and must be planned for in order to avoid disruption in service.
Historical weather data provide a key metric to predict power supply from renewable energy. The most effective strategy is to develop sophisticated and long-term weather models prior to renewable energy siting decisions. These models, along with sensors at the power source, provide a means for close monitoring and performance evaluation.
Different sources of renewable energy require different levels of modeling and prediction. Some renewable sources are very variable and others are much more consistent.
Wind power is much more variable than solar. Wind speeds, wind direction, and air density all factor into wind turbine performance. Turbines depend on consistent mid-range wind speeds, because if the wind is too low it can’t generate electricity and if it’s too high the turbines need to be shut down to avoid damage.
Solar energy is an easier energy source to predict, because the amount of light energy in a specific spot is fairly consistent, with some seasonal fluctuation, but greatest intensity in times of greatest demand. Power producers are working on ways to extend the availability of solar power after dark by deploying such ideas as thermal power generation and storage. Generating steam from the sun rather than direct conversion to electricity provides a more consistent and longer lasting power.
Hydro power is one of the more consistent and reliable renewable energy sources. Hydro plants that rely only on river flow aren’t all that common, with most drawing on a water reservoir for more consistent power.
Location is the most important factor when determining the variability or consistency of renewable energy. Siting renewable energy close to the source, but also close to demand, is a problem that require spatial analysis and modeling in order to optimize the performance and predictability of the power source.
Areas with consistent wind are typically at higher elevations or closer to the coast, and sea breezes are much more predictable and consistent than breezes on land. Solar power relies on clear skies, and some areas are much more ideal than others given their amount of sunshine. Hydro power relies on consistent and abundant water sources in areas that aren’t susceptible to drought.
A single wind turbine will be much more variable than a large array of turbines that are gridded together. Solar power can be complementary to wind, because wind is often strongest at night and during cloudy weather. Wind can also be a good complement to hydro power. As more renewable energy comes online, there will be a number of strategies deployed to balance intermittent supply with demand, and to align different sources to ease fluctuation.
The larger the electric grid, the less a factor intermittent power is to the overall grid. But with large percentages of renewable power on smaller grids, the grid design needs to made more intelligent and adaptable in order to manage fluctuations.
The viability and reliability of prediction models will improve over time as more data is collected and analyzed. The most variable, and most efficient, source of wind power is relatively new and the models are improving significantly each year.
Forecasting and predicting demand and supply are going to be of increasing importance as our power generation sources become more diverse and more distributed. Matching power demand with a wide variety of power sources also calls for a more sophisticated and intelligent transmission and distribution network.